Ant colony optimization

Dorigo, Marco

doi:10.4249/scholarpedia.1461

Cited by 167 publications

(73 citation statements)

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“…ACO simulates the above described behaviour of real ants to solve combinatorial optimization problems with artificial ants. Artificial ants find solutions on a graph in parallel processes using a constructive mechanism guided by artificial pheromone trails and a greedy heuristic known as visibility [10]. Pheromone trail intensity τij between nodes i and j represents the collective memory of ants and its amount is proportional to the quality of the solution generated.…”

Section: Ant Colony Optimization Based Approach (Nvr-aco)mentioning

confidence: 99%

An ant colony optimization approach for solving the nuclear magnetic resonance structure based assignment problem

Aslanov

Çatay

Apaydın

2013

Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation

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Nuclear Magnetic Resonance (NMR) Spectroscopy is an important technique that allows determining protein structure in solution. An important problem in protein structure determination using NMR spectroscopy is the mapping of peaks to corresponding amino acids. Structure Based Assignment (SBA) is an approach to solve this problem using a template structure that is homologous to the target. Our previously developed approach NVR-BIP computed the optimal solution for small proteins, but was unable to solve the assignments of large proteins. NVR-TS extended the applicability of the NVR approach for such proteins, however the accuracies varied significantly from run to run.In this paper, we propose NVR-ACO, an Ant Colony Optimization (ACO) based approach to this problem. NVR-ACO is similar to other ACO algorithms in a way that it also consists of three phases: the construction phase, an optional local search phase and a pheromone update phase. But it has some important differences from other ACO algorithms in terms of solution construction and pheromone update functions and convergence rules. We studied the data set used in NVR-BIP and NVR-TS. Our new method finds optimal solutions for small proteins and achieves perfect assignment on EIN and higher accuracy on MBP compared to NVR-TS. It is also more robust.

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Section: Ant Colony Optimization Based Approach (Nvr-aco)mentioning

confidence: 99%

An ant colony optimization approach for solving the nuclear magnetic resonance structure based assignment problem

Aslanov

Çatay

Apaydın

2013

Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation

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“…Each of the pheromone values is initially decreased by a certain percentage. Each edge then receives an amount of additional pheromone proportional to the quality of the solution to which it belongs (there is one solution per ant) [22]. This procedure is repeatedly applied until a termination criterion is satisfied.…”

Section: The Ant Colony Algorithmmentioning

confidence: 99%

“…We set the lengths of the edges between the vertices to be proportional to the distances between the cities represented by these vertices and we associate pheromone values and heuristic values with the edges of the graph. Pheromone values are modified at runtime and represent the cumulated experience of the ant colony, while heuristic values are problem dependent values that, in the case of the TSP, are set to be the inverse of the lengths of the edges [22].…”

Section: The Ant Colony Algorithmmentioning

confidence: 99%

An ACO based functional module detection algorithm for protein interaction networks

Shi

Zhang

2011

Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine

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Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes and differ based on the composition, affinity and lifetime of the association. A vast amount of PPI data for various organisms is available from MIPS, DIP and other sources. The identification of functional modules in PPI network is of great interest because they often reveal unknown functional ties between proteins and hence predict functions for unknown proteins.However the noise in the PPI network and the complexity of the network structure present great challenges to the functional module detection problem. In this paper, we propose a flexible framework which integrates the topological features of the network and the Ant Colony Optimization (ACO) algorithm to solve the problem. We first create an reliability measurement of the protein-protein interaction to rebuild the PPI network. Then we reformulate the problem to an optimal path detecting problem from the perspective of information flow. Last, an ACO-based functional module detection method is proposed by simulating the ants' behavior. We evaluate the proposed technique on the yeast protein-protein interaction network with MIPS functional categories and compare it with several other existing techniques. Our experiments show that our approach achieves better accuracy than other existing methods.

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“…Dorigo et al proposed the first ACO algorithm, ant system (AS) [2,3]. Since then, several extensions have been developed [4][5][6] and widely applied in various problems [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…A local configuration at the pixel position Ii,j for computing the variation Vc(I i,j ) defined in(3). The pixel I i,j is marked with ant image.…”

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confidence: 99%

Adaptive edge detection using ant colony

Benhamza

Merabti

Séridi

2013

2013 8th International Workshop on Systems, Signal Processing and Their Applications (WoSSPA)

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In this paper, an adaptive edges detection method based on ant colony algorithm is presented. Ant colony algorithm is a swarm-based metaheuristic inspired by the self-organizing properties of ant colony in nature. Artificial ants in movement create a pheromone graph, which denotes data of edge image. Further behaviors were added to each ant in response to local stimuli: the ant can self-reproduce and lead its progenitors in an appropriate direction to enhance research in suitable areas and it can die too if it exceeds a specific age and so eliminate the ineffective search. Experimental results show the performance of this technique enriched with these behaviors. It provides a good segmentation, fast and adaptive in extracting edges for a variety of images.

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Ant colony optimization

Cited by 167 publications

References 0 publications

An ant colony optimization approach for solving the nuclear magnetic resonance structure based assignment problem

An ant colony optimization approach for solving the nuclear magnetic resonance structure based assignment problem

An ACO based functional module detection algorithm for protein interaction networks

Adaptive edge detection using ant colony

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